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Finding low-energy structures of ligand-protected clusters is challenging due to the enormous conformational space and the high computational cost of accurate quantum chemical methods for determining the structures and energies of conformers. Here, we adopted and utilized a kernel rigid regression based machine learning method to accelerate the search for low-energy structures of ligand-protected clusters. We chose the Au25(Cys)18 (Cys: cysteine) cluster as a model system to test and demonstrate our method. We found that the low-energy structures of the cluster are characterized by a specific hydrogen bond type in the cysteine. The different configurations of the ligand layer influence the structural and electronic properties of clusters. [ABSTRACT FROM AUTHOR]

A new approach for the calculation of eigenstates with the state-averaged (multi-layer) multi-configurational time-dependent Hartree (MCTDH) approach is presented. The approach is inspired by the recent work of Larsson [J. Chem. Phys. 151, 204102 (2019)]. It employs local optimization of the basis sets at each node of the multi-layer MCTDH tree and successive downward and upward sweeps to obtain a globally converged result. At the top node, the Hamiltonian represented in the basis of the single-particle functions (SPFs) of the first layer is diagonalized. Here p wavefunctions corresponding to the p lowest eigenvalues are computed by a block Lanczos approach. At all other nodes, a non-linear operator consisting of the respective mean-field Hamiltonian matrix and a projector onto the space spanned by the respective SPFs is considered. Here, the eigenstate corresponding to the lowest eigenvalue is computed using a short iterative Lanczos scheme. Two different examples are studied to illustrate the new approach: the calculation of the vibrational states of methyl and acetonitrile. The calculations for methyl employ the single-layer MCTDH approach, a general potential energy surface, and the correlation discrete variable representation. A five-layer MCTDH representation and a sum of product-type Hamiltonian are used in the acetonitrile calculations. Very fast convergence and order of magnitude reductions in the numerical effort compared to the previously used block relaxation scheme are found. Furthermore, a detailed comparison with the results of Avila and Carrington [J. Chem. Phys. 134, 054126 (2011)] for acetonitrile highlights the potential problems of convergence tests for high-dimensional systems. [ABSTRACT FROM AUTHOR]

We have designed a [Fe(SH)4H]− model with the fifth proton binding either to Fe or S. We show that the energy difference between these two isomers (∆E) is hard to estimate with quantum-mechanical (QM) methods. For example, different density functional theory (DFT) methods give ∆E estimates that vary by almost 140 kJ/mol, mainly depending on the amount of exact Hartree–Fock included (0%–54%). The model is so small that it can be treated by many high-level QM methods, including coupled-cluster (CC) and multiconfigurational perturbation theory approaches. With extrapolated CC series (up to fully connected coupled-cluster calculations with singles, doubles, and triples) and semistochastic heat-bath configuration interaction methods, we obtain results that seem to be converged to full configuration interaction results within 5 kJ/mol. Our best result for ∆E is 101 kJ/mol. With this reference, we show that M06 and B3LYP-D3 give the best results among 35 DFT methods tested for this system. Brueckner doubles coupled cluster with perturbaitve triples seems to be the most accurate coupled-cluster approach with approximate triples. CCSD(T) with Kohn–Sham orbitals gives results within 4–11 kJ/mol of the extrapolated CC results, depending on the DFT method. Single-reference CC calculations seem to be reasonably accurate (giving an error of ∼5 kJ/mol compared to multireference methods), even if the D1 diagnostic is quite high (0.25) for one of the two isomers. [ABSTRACT FROM AUTHOR]

Clustering has become an indispensable tool in the presence of increasingly large and complex datasets. Most clustering algorithms depend, either explicitly or implicitly, on the sampled density. However, estimated densities are fragile due to the curse of dimensionality and finite sampling effects, for instance, in molecular dynamics simulations. To avoid the dependence on estimated densities, an energy-based clustering (EBC) algorithm based on the Metropolis acceptance criterion is developed in this work. In the proposed formulation, EBC can be considered a generalization of spectral clustering in the limit of large temperatures. Taking the potential energy of a sample explicitly into account alleviates requirements regarding the distribution of the data. In addition, it permits the subsampling of densely sampled regions, which can result in significant speed-ups and sublinear scaling. The algorithm is validated on a range of test systems including molecular dynamics trajectories of alanine dipeptide and the Trp-cage miniprotein. Our results show that including information about the potential-energy surface can largely decouple clustering from the sampled density. [ABSTRACT FROM AUTHOR]

We introduce a Monte-Carlo method that allows for the simulation of a polymeric phase containing a weak polyelectrolyte, which is coupled to a reservoir at a fixed pH, salt concentration, and total concentration of a weak polyprotic acid. The method generalizes the established grand-reaction method by Landsgesell et al. [Macromolecules 53, 3007–3020 (2020)] and, thus, allows for the simulation of polyelectrolyte systems coupled to reservoirs with a more complex chemical composition. In order to set the required input parameters that correspond to a desired reservoir composition, we propose a generalization of the recently published chemical potential tuning algorithm of Miles et al. [Phys. Rev. E 105, 045311 (2022)]. To test the proposed tuning procedure, we perform extensive numerical tests for both ideal and interacting systems. Finally, as a showcase, we apply the method to a simple test system that consists of a weak polybase solution that is coupled to a reservoir containing a small diprotic acid. The complex interplay of the ionization of various species, the electrostatic interactions, and the partitioning of small ions leads to a non-monotonous, stepwise swelling behavior of the weak polybase chains. [ABSTRACT FROM AUTHOR]

A multivariant statistical approach was used to identify treatment conditions that improve the survivability of structural reactive material (SRM) projectiles upon launch and enhance energy release upon impact. The study included both mechanical testing of projectiles as well as their reactive characterization. The projectiles were launched in a high-velocity impact-ignition testing system and impacted an anvil for vented chamber calorimetry. This study examined a link between ultimate compressive stress and combustion performance. Two treatments were applied to consolidated aluminum projectiles including annealing and addition of silica (SiO2) inclusions. Results showed annealing at moderate temperatures resulted in intact SRM projectiles upon launch. Adding small concentrations (1–2 wt. %) of SiO2 to the SRM promoted fragmentation and combustion performance upon impact. Compared to the untreated projectiles, annealing with SiO2 inclusion processing treatments improved the energy conversion efficiency from 37–84% (for untreated projectiles) up to 54–98%. Increasing interparticle dislocation recovery by annealing while balancing inclusions promoting fragmentation upon impact was the key to optimizing combustion performance for SRM ballistic impact applications. [ABSTRACT FROM AUTHOR]

We present equations of motion (EOMs) for general time-dependent wave functions with exponentially parameterized biorthogonal basis sets. The equations are fully bivariational in the sense of the time-dependent bivariational principle and offer an alternative, constraint-free formulation of adaptive basis sets for bivariational wave functions. We simplify the highly non-linear basis set equations using Lie algebraic techniques and show that the computationally intensive parts of the theory are, in fact, identical to those that arise with linearly parameterized basis sets. Thus, our approach offers easy implementation on top of existing code in the context of both nuclear dynamics and time-dependent electronic structure. Computationally tractable working equations are provided for single and double exponential parametrizations of the basis set evolution. The EOMs are generally applicable for any value of the basis set parameters, unlike the approach of transforming the parameters to zero at each evaluation of the EOMs. We show that the basis set equations contain a well-defined set of singularities, which are identified and removed by a simple scheme. The exponential basis set equations are implemented in conjunction with the time-dependent modals vibrational coupled cluster (TDMVCC) method, and we investigate the propagation properties in terms of the average integrator step size. For the systems we test, the exponentially parameterized basis sets yield slightly larger step sizes compared to the linearly parameterized basis set. [ABSTRACT FROM AUTHOR]

Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and material properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncertainty quantification efforts have primarily leveraged the standard deviation of predictions across an ensemble of independently trained neural networks. This incurs a large computational overhead in both training and prediction, resulting in order-of-magnitude more expensive predictions. Here, we propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. This allows us to obtain uncertainty estimates with virtually no additional computational overhead over standard training and inference. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles. We further examine the uncertainty estimates of our methods and deep ensembles across the configuration space of our test system and compare the uncertainties to the potential energy surface. Finally, we study the efficacy of the method in an active learning setting and find the results to match an ensemble-based strategy at order-of-magnitude reduced computational cost. [ABSTRACT FROM AUTHOR]

We present an unsupervised data processing workflow that is specifically designed to obtain a fast conformational clustering of long molecular dynamics simulation trajectories. In this approach, we combine two dimensionality reduction algorithms (cc_analysis and encodermap) with a density-based spatial clustering algorithm (hierarchical density-based spatial clustering of applications with noise). The proposed scheme benefits from the strengths of the three algorithms while avoiding most of the drawbacks of the individual methods. Here, the cc_analysis algorithm is applied for the first time to molecular simulation data. The encodermap algorithm complements cc_analysis by providing an efficient way to process and assign large amounts of data to clusters. The main goal of the procedure is to maximize the number of assigned frames of a given trajectory while keeping a clear conformational identity of the clusters that are found. In practice, we achieve this by using an iterative clustering approach and a tunable root-mean-square-deviation-based criterion in the final cluster assignment. This allows us to find clusters of different densities and different degrees of structural identity. With the help of four protein systems, we illustrate the capability and performance of this clustering workflow: wild-type and thermostable mutant of the Trp-cage protein (TC5b and TC10b), NTL9, and Protein B. Each of these test systems poses their individual challenges to the scheme, which, in total, give a nice overview of the advantages and potential difficulties that can arise when using the proposed method. [ABSTRACT FROM AUTHOR]

Efficiently identifying the most important communities and key transition nodes in weighted and unweighted networks is a prevalent problem in a wide range of disciplines. Here, we focus on the optimal clustering using variational kinetic parameters, linked to Markov processes defined on the underlying networks, namely, the slowest relaxation time and the Kemeny constant. We derive novel relations in terms of mean first passage times for optimizing clustering via the Kemeny constant and show that the optimal clustering boundaries have equal round-trip times to the clusters they separate. We also propose an efficient method that first projects the network nodes onto a 1D reaction coordinate and subsequently performs a variational boundary search using a parallel tempering algorithm, where the variational kinetic parameters act as an energy function to be extremized. We find that maximization of the Kemeny constant is effective in detecting communities, while the slowest relaxation time allows for detection of transition nodes. We demonstrate the validity of our method on several test systems, including synthetic networks generated from the stochastic block model and real world networks (Santa Fe Institute collaboration network, a network of co-purchased political books, and a street network of multiple cities in Luxembourg). Our approach is compared with existing clustering algorithms based on modularity and the robust Perron cluster analysis, and the identified transition nodes are compared with different notions of node centrality. [ABSTRACT FROM AUTHOR]

Power system operators are faced with the problem of unit commitment belonging to mixed integer programming, which becomes very complicated, as units become large-scale and highly constrained. Because unit commitment problem is a binary problem with commitment and de-commitment, a discrete/binary optimization algorithm with superior performance is required. This paper proposes a novel hybrid binary bat algorithm for unit commitment problem, which consists of two process. To begin with, the proposed binary bat algorithm is applied to determining the commitment schedule of unit commitment problem. Specifically, an improved crossover operator based on exponential-logic-modulo map is proposed to enhance the convergence and maintain the diversity of populations. To prevent the algorithm from falling into a local optimum, a local mutation strategy performs local perturbation. Chaotic map is responsible for updating some parameters to increase the performance of the proposed algorithm. Furthermore, Lambda-iteration method is adopted to solve economic load dispatch in continuous space. Constraint handling is performed using the heuristic constraint produce. The effectiveness of the proposed algorithm is verified by benchmark functions and test systems. Additionally, the simulation results are compared with other well-established heuristic and binary approaches. [ABSTRACT FROM AUTHOR]

Rationale: Mass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix‐assisted laser desorption/ionization [MALDI], MALDI‐2, and infrared matrix‐assisted laser desorption electrospray ionization [IR‐MALDESI]). Methods: A novel QC mix was sprayed across glass slides to collect QC/SST regions‐of‐interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary. Results: The PCA‐based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR‐MALDESI‐MSI analyses. This minimizes the type‐I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning. Conclusions: This study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms. [ABSTRACT FROM AUTHOR]

Small‐radius curved bridges are mostly used for overpass ramps, that are spatially irregular and usually have very complex seismic behavior. It is not easy to reproduce such behavior because of the need for large‐scale shaking tables. The hybrid test is one of the most effective approaches for solving this problem by considering the structural elements of interest as physically tested substructure while the rest is numerically simulated. In this paper, a hybrid test system was first developed based on the OpenFresco framework, where one of the piers was considered as the tested substructure, and the rest was simulated by OpenSees. A novel spatial loading device (SLD), configured as the Stewart pattern, was then developed to achieve the boundary conditions between substructures. The control schemes to perform the force‐displacement mixed control, conduct the geometric transformation while considering the load point offset, and achieve an external displacement control were proposed and validated through several rounds of hybrid testing. The experimental results indicate that the experimental system including loading control subsystem and hybrid control subsystem can realize the loading command accurately. [ABSTRACT FROM AUTHOR]

Molecular rings of N carbon atoms (cyclo[N]carbons, or CN) are excellent benchmarking systems for testing quantum chemical theoretical methods and valuable precursors to other carbon-rich materials. Odd-N cyclocarbons, which have been elusive to date, are predicted to be even less stable than even-N cyclocarbons. We report the on-surface synthesis of cyclo[13]carbon, C13, by manipulation of decachlorofluorene with a scanning probemicroscope tip. We elucidated the properties of C13 by experiment and theoretical modeling. C13 adopts an open-shell configuration with a triplet ground state and a kinked geometry, which shows different extents of distortion and carbene localization depending on themolecular environment. Moreover, we prepared and characterized the C13 dimer, cyclo[26]carbon, demonstrating the potential of cyclocarbons and their precursors as building blocks for carbon allotropes. [ABSTRACT FROM AUTHOR]

The term pyric herbivory was first introduced in 2009, describing how fire shapes herbivory as burned areas attract herbivores and, simultaneously, herbivory shapes fuel load and fire behaviour. Pyric herbivory results in a mosaic of patches with varying levels of herbivory and grazing intensity fire intensity and frequency. The importance of pyric herbivory for ecosystem heterogeneity and biodiversity has been described for North American, Australian and African systems, but the concept remains largely untested in a European context.We introduced fire and herbivory in a full‐factorial experiment in a temperate European wood‐pasture system to test whether pyric herbivory operates in ways comparable to grassy systems elsewhere in the world. Using camera traps, we observed the behaviour of cattle in burned subplots (49 m2) compared with unburned subplots. We measured grass height and the proportion of the subplot that burned as variables affecting cattle preference and to assess how grazing affects fire behaviour. We also examined the effect on plant species and life‐form composition after six seasons of treatment.Cattle spent more time grazing in burned than in unburned subplots in the most productive paddock, where a larger proportion of the subplot burned. The proportion of a subplot that burned was positively related to pre‐fire grass height. Moreover, both grass height and the proportion of subplot burned declined in the burned subplots during the 6‐year study period and fire and cattle grazing altered the relative cover of graminoids and shrubs (Rubus spp.), with more graminoids in grazed and/or burned subplots and more shrubs in ungrazed subplots at the end of the study.Synthesis and applications. In our temperate European wood pasture, fire and (cattle) grazing interacted in ways comparable to pyric herbivory in grassy ecosystems elsewhere in the world, especially in the most productive paddock. Fire attracted grazing, with cattle grazing longer on subplots that burned more fully. Grazing also affected fire, where over the course of our experiment cattle grazing reduced grass height and the proportion of a subplot that burned. We suggest that pyric herbivory is an interesting management method to further explore in the European context to address the loss of biodiversity in open ecosystems, particularly in more productive sites. [ABSTRACT FROM AUTHOR]

This paper introduces a virtual consensus‐based wide area differential protection method through cooperative control concepts and graph theory. Using multi‐agent systems eliminates the need for a direct telecommunication connection between the protective relays to implement the proposed differential protection. In addition, applying telecommunication subgraphs facilitates the establishment of numerous differential protection areas. Collaboration between protected areas is facilitated by common agents, establishing a wide‐area cooperative protection network. The capability of the network operator to define various protection areas and the collaboration between these areas ensure the versatility of the proposed method for various protection purposes. The present paper primarily represents the application of the proposed protection system as a wide‐area supervisor protection for distance relays. Performance evaluations on an IEEE 39‐bus test system illustrate the method's effectiveness in various scenarios. The results show enhanced power system stability and resilience, particularly when traditional methods struggle to detect power swings with high impedance variation rates. [ABSTRACT FROM AUTHOR]

The global warming crisis, together with environmental concerns, has already led governments to replace traditional fossil‐fuel cars with low‐emission Electric Vehicles (EVs). This replacement has resulted in the addition of a large number of EVs capable of connecting to the grid. A coordinated charging method is needed to promote EVs on a large scale while preventing grid overloading. In this work, EVs are categorized based on their battery capacity, as cars, trucks, and buses. A charging/discharging mechanism based on Real Time Pricing for each hour is developed for a 20‐min block that has been formed. The locations of EV charging stations have been identified using Voltage Stability Index. In the present work, the power loss when Distributed Generator (DG) units are deployed after the EVs are located in the system for charging and discharging is analyzed. The DGs are considered as solar‐based or wind‐based generators for studying the economic benefit of such an investment under two different weather scenarios and topologies on standard test systems. The results thus obtained give an insight into the various technical and economic benefits. [ABSTRACT FROM AUTHOR]

The results of evaluating the adsorption and fluorescent properties of azolotriazine compounds and phases based on cadmium sulfide quantum dots in vapors of volatile organic compounds (biomarkers of living systems) are considered. The adsorption properties of phases based on organic dyes and encapsulated semiconductors towards vapors of alcohols, ketones, amines, acids, ammonia, and aldehydes are investigated using direct high-sensitivity piezoelectric quartz microweighing. The spectral properties and their variations in analyte vapors are investigated using various spectroscopy methods (absorption, photoluminescence). The research results are compared, and the potential for optimizing this stage is assessed. It is proposed to evaluate the consistency of methods for predicting changes in fluorescent properties in test systems for volatile organic compounds using the Kendall coefficient of concordance, W. The highest level of agreement (W = 0.89) was observed between the methods of spectrofluorimetry and direct vapor microweighing upon adsorption on phases being potential fillers of test systems. A correlation between the results obtained by different fluorescence methods (visual plate test systems and fluorimetry of phases on paper substrates) is 0.80, which confirms a high degree of consistency in assessments using them in the degree of interaction between the analytes and organic, combined fluorochromes. The methods closely related in the nature of the analytical response do not exhibit better agreement than the method of direct vapor microweighing on microphases of fluorimetric reagents of different nature (cadmium sulfide quantum dots/chitosan, organic azolotriazine compounds, mixed phases). This, in turn, enables the selection of simpler, more available, and more rapid methods and means of analysis in routine experiments. [ABSTRACT FROM AUTHOR]

Infections caused by snakehead vesiculovirus (SHVV) have seen frequent outbreaks in recent years, inflicting significant losses on the snakehead aquaculture industry. Early detection is therefore essential for effective prevention and control of pathogenic infections and reduction of economic losses caused by infections. There is an urgent need for a simple, rapid, specific, sensitive, and intuitive method to monitor snakehead infected with SHVV. The aim of the present study was to develop and evaluate a loop-mediated isothermal amplification (LAMP) assay for the rapid visual detection of SHVV in snakehead. Three pairs of primers were designed according to the conserved region of phosphoprotein (P) gene sequences of SHVV and were applied for the detection of SHVV from fish samples. Time and temperature conditions for the amplification of SHVV were optimized at 65 °C and 55 min. The LAMP assay demonstrated high specificity, with no cross-reactivity with seven other viruses. Amplification results were visualized by a color change after the addition of hydroxynaphthol blue (HNB) dye. Sensitivity test results showed that the minimum detection volume with this method was 1.76 × 102 copies/μL, which was 100 times more sensitive than RT-PCR assay. We used the established LAMP system to test 50 clinical samples and detected 32 positive responses, whereas 22 positive samples out of 50 samples were detected by RT-PCR. The establishment of a visual LAMP assay further shortens the virus detection process and allows visual reading of positive responses through color changes; it is suitable for use in quarantine and field detection. Therefore, this proposed method provides a sensitive, specific, and user-friendly method for the rapid diagnosis of SHVV in snakehead farming. [ABSTRACT FROM AUTHOR]

The reconfiguration of electrical distribution systems has been addressed in numerous scientific studies. Thus, several techniques and models are considered, especially to reduce electrical losses and improve the voltage profile. However, it is clear that even with the increase in research, which has been accentuated since the last decade, the locational pricing and the transmission costs allocation is little explored. In this regard, the proposed method is a distribution system reconfiguration (DSR) optimization problem focused on minimizing the use of the transmission system. In order to validate the meta‐heuristic employed for the DSR, the IEEE 33 and 69 node test systems are used as a proof of concept. In addition, case studies with four substations, three different transmission cost allocation techniques, and transmission system capacity scenarios are analyzed. Numerical results showed an important transmission expansion deferral with consequent decrease in overall costs compared to the classic DSR. The transmission network use of system (TNUoS) charges can play a crucial role in reducing the total cost of the electrical system, promoting distributors to change their feeder patterns so that a larger share of loads access substations with lower charges. [ABSTRACT FROM AUTHOR]

In distribution systems, the state estimation task is very complicated due to the problems arising from the unbalanced operation and network configuration changes. Additionally, the convergence process of state estimation gets affected by the loss of physical measurements due to meter failure, communication failure, and large communication time delays. To address the issue of loss of measurement data, a new deep learning approach, namely a convolutional generative adversarial imputed network (CGAIN) with modified loss functions, is proposed. A branch current-distribution system state estimation (BC-DSSE) with different percentages of missing measurement data has been implemented and studied. Furthermore, the analysis has been carried out for different types of uncertainties involving several kinds of measurement devices and pseudo-measurements for different classes of consumer loads. Also, the effect of imputed data on state estimation accuracy is tested at different operating conditions of load. The imputation performance of the CGAIN model is tested by comparing it with statistical methods, and ML/DL approaches. The benefits of the proposed model are validated by Monte Carlo simulations on the modified IEEE 13-node and IEEE 37-node unbalanced distribution test systems. Simulation results show that the proposed model can effectively impute the missed data under any operating condition. Further, the robustness of the algorithm is checked with the largest normalized residual (LNR) test for minimizing the effect of bad data. [ABSTRACT FROM AUTHOR]

Rolling contact fatigue (RCF) cracks online detection using eddy current testing (ECT) is in urgent demand. However, RCF cracks detection and evaluation in this way under moving conditions remains challenging due to the velocity effect in ECT. This paper aims to study the response of a transmitter-receiver eddy current probe to cracks under moving conditions and evaluate the depth and inclination angle of RCF cracks. In this paper, a high-speed eddy current testing system is developed to experimentally investigate the influence of coil gap, detection speed, and the lift-off on the eddy current probe's response under moving conditions. In addition, the temporal and amplitude features of the eddy current signal are extracted to characterise the depth and inclination angle of the RCF cracks. The experimental results indicate the eddy current probe's response can be improved by increasing the coil gap (coil centre distance) suitably, which can be done to compensate for the attenuation of the eddy current signal caused by detection speed and lift-off. The probe's response hardly changes with an increase in detection speed when the driver and pick-up coils of the eddy current probe completely overlap. The crack depth and inclination angle can be evaluated under moving conditions. [ABSTRACT FROM AUTHOR]

Adult neurotoxicity (ANT) and developmental neurotoxicity (DNT) assessments aim to understand the adverse effects and underlying mechanisms of toxicants on the human nervous system. In recent years, there has been an increasing focus on the so-called new approach methodologies (NAMs). The Organization for Economic Co-operation and Development (OECD), together with European and American regulatory agencies, promote the use of validated alternative test systems, but to date, guidelines for regulatory DNT and ANT assessment rely primarily on classical animal testing. Alternative methods include both non-animal approaches and test systems on non-vertebrates (e.g., nematodes) or non-mammals (e.g., fish). Therefore, this review summarizes the recent advances of NAMs focusing on ANT and DNT and highlights the potential and current critical issues for the full implementation of these methods in the future. The status of the DNT in vitro battery (DNT IVB) is also reviewed as a first step of NAMs for the assessment of neurotoxicity in the regulatory context. Critical issues such as (i) the need for test batteries and method integration (from in silico and in vitro to in vivo alternatives, e.g., zebrafish, C. elegans) requiring interdisciplinarity to manage complexity, (ii) interlaboratory transferability, and (iii) the urgent need for method validation are discussed. [ABSTRACT FROM AUTHOR]

At present, the usage of various energy sources in the electricity grid has formed new challenges like power quality effectiveness, energy utilization, and voltage stabilization. With the recognition of smart grid, the maintenance of power quality (PQ) has been regarded as the challenging one. Primarily, APF are employed in an attempt to recompense the issues associated with power quality, as they can filter out promptly and responds vibrantly. Here, in this work, an IEEE 9 Bus System based on WSCC is introduced along with UPQC combined DPFC system. To accommodate a renewable energy source, one of the generators test system. MGWO approach-based PID device is combined with DC Source as a converter for 3-phase inverter. This arrangement is studied using Simulation in both cases with and without DPFC. In addition, the performance estimation is done in terms of parameters like load voltage and current, shunt voltage and series current, and grid current along with their THD values. Based on the outcome, it is found that the proposed UPQC–DPFC combination helps in effective harmonic elimination and increased power storage at the output side. [ABSTRACT FROM AUTHOR]

This research investigates the role of various energy storage systems (ESS) in improving the power system resiliency. Different ESS configurations are analyzed individually and in combination to assess their impact on system performance. Additionally, a fuzzy-based controller (PIλDN + FFOPI) is proposed as a secondary controller for load frequency control, with control parameters optimized using an improved Volleyball Premier League algorithm (IVPL). This algorithm leverages the powerful synergy of opposition-based learning and the versatility of the VPL algorithm, resulting in a highly effective and innovative approach. To validate the proposed approach, the modified Kundur's 2-area test system under deregulation is first utilized, and its effectiveness is confirmed through testing with both step and random load disturbances. Sensitivity analysis is conducted to evaluate system performance across a wide range of parameter variations. Furthermore, the modified IEEE 39 bus system is utilized to assess compatibility and scalability. Results demonstrate the effectiveness of combined ESS configurations and the fuzzy-based controller in enhancing system stability and reliability. This research contributes to power system engineering by offering insights into the benefits of energy storage systems for dynamic response enhancement. The proposed fuzzy-based control strategy, tuned by the IVPL algorithm, presents a promising approach for improving power system performance and stability. [ABSTRACT FROM AUTHOR]

Extreme events bring great challenges to the safety and resilience of a distribution network, which makes it difficult for traditional static resources to provide enough operation support. The continuous enrichment of spatial‐temporal flexibility resources on both the power demand and supply sides provides a new idea for the dynamic dispatching of distribution network. This paper proposes a spatial‐temporal migration method of mobile power sources (MPSs) on the supply side and internet data centres (IDCs) on the demand side, based on the power‐transportation and power‐computation network coupling models respectively. Then, the dynamic dispatching potential of MPSs and IDCs is evaluated, and the dispatching models against extreme events of MPSs and IDCs are established. With the objective of maximizing system resilience and minimizing dispatching costs, an optimization model considering spatial‐temporal migration of data loads and MPSs dynamic dispatching strategy is formulated. Finally, the proposed method is tested in the modified IEEE 33‐node test system and IEEE 69‐node test system, and the economy and resilience enhancement are verified with comparison of different dispatching schemes. [ABSTRACT FROM AUTHOR]

To enhance the resilience of transmission systems with offshore wind farms before the advent of typhoons, a proactive unit commitment scheme is proposed. A novel scenario tree is proposed to quantify the uncertain impacts of typhoons on offshore wind farms, transmission lines, and system states, where the inertia support from offshore wind farms and random system state are in‐cooperated. The frequency security constraint is captured by a multi‐variative piece‐wise linear function, where the frequency nadir are simulated under varying inertia and disturbance combinations. The unit commitment problem is formulated as a two‐stage stochastic optimisation problem with nonlinear recourse. This problem is solved by a multi‐cuts Benders decomposition algorithm with finite iteration convergence property. Simulations are conducted on a modified IEEE‐RTS 79 test system with offshore wind farms. Results verify the effectiveness of the proposed model regarding the load shedding and frequency nadir enhancement. [ABSTRACT FROM AUTHOR]

The multi-objective economic load dispatch problem (ELDP) with non-smooth cost functions and ramprate limits presents a challenging optimization task in power systems. This paper proposes the use of a modified cheetah optimizer (MCO) that incorporates opposition-based learning (OBL) and a dynamic adaptive weighting factor to efficiently solve this problem. The simulations are conducted on standard test systems using MATLAB programming. A comparative study is performed, evaluating the performance of the MCO against basic CO and other similar heuristics. The results demonstrate the effectiveness of the MCO in achieving optimal solutions for the multi-objective ELDP with non-smooth cost functions and ramp-rate limits. The proposed approach offers a promising solution for addressing the complex optimization requirements in power system operation and planning. The optimal cost is determined using multi-criterion optimization (MCO) in 3-bus system as $6,838.6434/h, $7,738.789/h, and $8,252.033/h for 700 MW, 800 MW and 850 MW demand levels respectively. The optimal cost is evaluated as $17,988.96/h for the 13-bus test system considering a total demand of 1800 MW. The optimal cost is evaluated as 121960.30 $/hr, for the 40-bus test system considering a total demand of 10500 MW. These outcomes demonstrate the efficacy of MCO in resolving the ELDP with generator and valve controls. [ABSTRACT FROM AUTHOR]

In AI pandemic applications, the online automatic AI recording apparatus for official councils such as court trials, business conferences and commercial meetings will become imperative because it could let the opinion identification and consensus of participants be synchronically available to implicitly diminish social costs such as follow-up disputes and controversies. Hence, in this study, an automatic on-line multi-dialogue recording system is completely constructed, where the unbounded interleaved-state recurrent neural networks (UIS-RNN) with proposed crux improvements is exploited to achieve confident speaker-diarization. For keeping the systematic robustness, a denoising spectral-LSTM, which is precisely modified from the dual-signal transformation LSTM (DTLN), can strengthen its subsequent crux-improved UIS-RNN and automatic speech recognition (ASR). Finally, the MacBERT model is set to rectify the possible wrong words in conversed sentences according to the learned rational context. For making our system being a practical software apparatus in the use of unmarked multi-person councils, we have also completed the convenient interfaces for the operations of ASR and speaker-diarization, which can exhibit on-line denoising efficacy and speaker-diarization results as well as offer real-time hand-crafted rectifications to common users. In extensive experiments, the proposed recording system can promise high accuracy rates of online speaker diarization and speech-separated ASR. Our proposed system had been examined by the cooperated law court staffs, who offered the noise-embedded speeches of practical court field to test our system. Since the tight recording burden had been indeed noticeably alleviated in their legal-action councils, the court staffs had endorsed that the proposed entire system could be a friendly labor-saving AI apparatus for on-line automatic multi-dialogue recording. [ABSTRACT FROM AUTHOR]

Testing autonomous driving systems for safety and reliability is essential, yet complex. A primary challenge is identifying relevant test scenarios, especially the critical ones that may expose hazards or harm to autonomous vehicles and other road users. Although numerous approaches and tools for critical scenario identification are proposed, the industry practices for selection, implementation, and evaluation of approaches, are not well understood. Therefore, we aim at exploring practical aspects of how autonomous driving systems are tested, particularly the identification and use of critical scenarios. We interviewed 13 practitioners from 7 companies in autonomous driving in Sweden. We used thematic modeling to analyse and synthesize the interview data. As a result, we present 9 themes of practices and 4 themes of challenges related to critical scenarios. Our analysis indicates there is little joint effort in the industry, despite every approach has its own limitations, and tools and platforms are lacking. To that end, we recommend the industry and academia combine different approaches, collaborate among different stakeholders, and continuously learn the field. The contributions of our study are exploration and synthesis of industry practices and related challenges for critical scenario identification and testing, and potential increase of industry relevance for future studies. [ABSTRACT FROM AUTHOR]

Test inputs fail not only when the system under test is faulty but also when the inputs are invalid or unrealistic. Failures resulting from invalid or unrealistic test inputs are spurious. Avoiding spurious failures improves the effectiveness of testing in exercising the main functions of a system, particularly for compute-intensive (CI) systems where a single test execution takes significant time. In this article, we propose to build failure models for inferring interpretable rules on test inputs that cause spurious failures. We examine two alternative strategies for building failure models: (1) machine learning (ML)-guided test generation and (2) surrogate-assisted test generation. ML-guided test generation infers boundary regions that separate passing and failing test inputs and samples test inputs from those regions. Surrogate-assisted test generation relies on surrogate models to predict labels for test inputs instead of exercising all the inputs. We propose a novel surrogate-assisted algorithm that uses multiple surrogate models simultaneously, and dynamically selects the prediction from the most accurate model. We empirically evaluate the accuracy of failure models inferred based on surrogate-assisted and ML-guided test generation algorithms. Using case studies from the domains of cyber-physical systems and networks, we show that our proposed surrogate-assisted approach generates failure models with an average accuracy of 83%, significantly outperforming ML-guided test generation and two baselines. Further, our approach learns failure-inducing rules that identify genuine spurious failures as validated against domain knowledge. [ABSTRACT FROM AUTHOR]

A mechanistic understanding of algal growth is essential for maintaining a sustainable environment in an era of climate change and population expansion. It is known that algal growth is tightly controlled by complex interactive physical and chemical conditions. Many mathematical models have been proposed to describe the relation of algal growth and environmental parameters, but experimental verification has been difficult due to the lack of tools to measure cell growth under precise physical and chemical conditions. As such, current models depend on the specific testing systems, and the fitted growth kinetic constants vary widely for the same organisms in the existing literature. Here, we present a microfluidic platform where both light intensity and nutrient gradients can be well controlled for algal cell growth studies. In particular, light shading is avoided, a common problem in macroscale assays. Our results revealed that light and nitrogen colimit the growth of algal cells, with each contributing a Monod growth kinetic term in a multiplicative model. We argue that the microfluidic platform can lead towards a general culture system independent algal growth model with systematic screening of many environmental parameters. Our work advances technology for algal cell growth studies and provides essential information for future bioreactor designs and ecological predictions. [ABSTRACT FROM AUTHOR]

Background: A recently published article, by Reiber et al., on the representativity of macroinvertebrate communities in outdoor micro- or mesocosm studies, used as a higher tier tool in the environmental risk assessment of plant protection products (PPPs) in the EU, concluded that 'micro-/mesocosm studies do not represent natural macroinvertebrate communities'. Fundamentally, the article based its conclusion on the analysis of data from 26 streams used in a monitoring project in Germany (2018–2019), in comparison to taxa found in seven lentic micro- and mesocosm studies, conducted at four test sites (2013 – 2018), and submitted to the UBA, Germany. Results: There are multiple reasons why this conclusion is incorrect, e.g. the number of taxa, for which the Minimum Detectable Differences (MDDs) were low enough to allow a detection of direct effects in the seven lentic mesocosm studies, cannot be compared to the number of taxa just present in at least five of 26 streams. We have further investigated the data from five of the seven studies which were analysed in detail by Reiber et al. and determined that the MDDs of 12 to 18 invertebrate taxa per study fulfilled the current recommendation to allow a detection of medium effects (MDD up to 70%). However, which taxa can be considered potentially sensitive depends on the specific test item. While lentic test systems may not be suitable to test effects on typical stream taxa, taxa occurring in lentic systems such as ponds and ditches are not by definition less sensitive, or vulnerable, to pesticides than taxa living in streams, and their relative sensitivity can be checked in laboratory tests, or artificial streams, if needed. Conclusions: In our view, well conducted micro- and mesocosm studies do provide reliable and useful data for the environmental risk assessment of plant protection products covering long-term, as well as indirect, effects under semi-natural conditions. [ABSTRACT FROM AUTHOR]

BACKGROUND: The landing error scoring system test is a standard for assessing the risk of non-contact injuries and has not yet been developed for Chinese college soccer programs. OBJECTIVE: To establish a test evaluation standard for the landing error scoring system to provide a basis for evaluating the risk of non-contact injuries in college soccer students. METHODS: A prospective cohort study was designed in which 219 athletes from 10 college soccer teams were tested with the standard landing error scoring system, and the subjects were followed up by questionnaires and medical examinations for non-contact injuries of the lower extremities and trunk for 1 year after testing to determine sex differences and assessment criteria for the landing error scoring system test indicators. RESULTS AND CONCLUSION: The total score of the landing error scoring system was (8.22±1.65) points for 219 subjects, (8.29±1.74) for males and (8.07±1.44) for females, with no significant difference between males and females (P > 0.05). Within 1 year after the test, the overall injury rate of 219 subjects was 10.05% and the morbidity rate was 15.98%; the injury rate of male subjects with non-contact injury of the lower limbs and trunk was 12.75% and the morbidity rate was 20.13%; the injury rate of female subjects with non-contact injury of the lower limbs and trunk was 4.29% and the morbidity rate was 7.14%. There were no significant differences in the injury rate between men and women (P < 0.05). The total score of the landing error scoring system was higher in the injury group than in the non-injury group [(9.50±1.14) vs. (8.08±1.64), P < 0.01]; for male subjects, the total score of the landing error scoring system was higher in the injury group than in the non-injury group [(9.63±1.12) vs. (8.09 ± 1.73), P < 0.01]. The area under the curve for the total score of the landing error scoring system was 0.773 (P=0.000), which had a diagnostic value for the risk of non-contact injury of the lower extremities and trunk in male subjects, with a best cut-off point of 8.5, sensitivity of 0.842, specificity of 0.623, positive likelihood ratio of 2.233, negative likelihood ratio of 0.254, relative risk factor of 8.400, and odds ratio of 8.816; the total score of the landing error scoring system was not applicable for assessing the risk of non-contact injury of the lower extremities and trunk in female subjects. To conclude, the landing error scoring system test can be used as a criterion to assess the risk of non-contact injury to the lower extremity and trunk in Chinese college male soccer players, with an optimal cut-off point of 8.5. The risk of non-contact injury to the lower extremity and trunk is 8.40 times higher in male athletes with a landing error scoring system test score of ≥ 8.5 than in male athletes with a score of < 8.5. [ABSTRACT FROM AUTHOR]

Modern vehicles equipped with Advanced Driver Assistance Systems (ADAS) rely heavily on sensor fusion to achieve a comprehensive understanding of their surrounding environment. Traditionally, the Kalman Filter (KF) has been a popular choice for this purpose, necessitating complex data association and track management to ensure accurate results. To address errors introduced by these processes, the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is a good choice. This alternative filter implicitly handles the association and appearance/disappearance of tracks. The approach presented here allows for the replacement of KF frameworks in many applications while achieving runtimes below 1 ms on the test system. The key innovations lie in the utilization of sensor-based parameter models to implicitly handle varying Fields of View (FoV) and sensing capabilities. These models represent sensor-specific properties such as detection probability and clutter density across the state space. Additionally, we introduce a method for propagating additional track properties such as classification with the GM-PHD filter, further contributing to its versatility and applicability. The proposed GM-PHD filter approach surpasses a KF approach on the KITTI dataset and another custom dataset. The mean OSPA(2) error could be reduced from 1.56 (KF approach) to 1.40 (GM-PHD approach), showcasing its potential in ADAS perception. [ABSTRACT FROM AUTHOR]

For the rapid determination of bisphenol A in drinking water, a membrane test system has been developed using a conjugate of gold nanoparticles with an aptamer that specifically binds the target analyte, and a conjugate of mercaptosuccinic acid with a carrier protein impregnated in the test zone of the strip. The working principle of the test system is based on the binding of free gold nanoparticles in the test zone, formed as a result of the competitive interaction of the aptamer with bisphenol A and its release from the surface of gold nanoparticles. Conjugates of gold nanoparticles with aptamers of different compositions were obtained and tested. Optimal conditions were selected to ensure the achievement of a low detection limit for bisphenol A. The developed test system allows the detection of bisphenol A within 15 min with a detection limit of 13.5 ng/mL. The suitability of the test system was confirmed when testing drinking water; the recovery values of bisphenol A ranged from 88.2 to 101.3%. [ABSTRACT FROM AUTHOR]

The supervisory control and data acquisition (SCADA) system is one of the key requirements for monitoring the power systems state. The power system state is estimated from the state estimation (SE) function, which uses the measurements results from different points. Inaccurate estimation may cause inefficient management of power systems and making decisions. Usually, the number of measurement units (MUs) is limited so the optimal location of MUs is very important in successful and accurate state estimation. The optimal placement of MUs gets more complicated when the actual state of the power system is uncertain due to the high penetration of renewable generation and load uncertainties. Considering the power system's actual state uncertainty and measurement units' inherent error, this work presents a probabilistic model for the uncertainty of measuring based on the K‐medoids data clustering technique. Then the probabilistic optimal placement of MUs is solved by a binary particle swarm optimization (PSO) method. Considering the mentioned uncertainties makes the obtained solution robust against all the probable scenarios. The proposed technique is implemented on the IEEE 14‐bus test system and the results are discussed. Results show the effectiveness of the proposed method in improving the accuracy of power system state estimation. [ABSTRACT FROM AUTHOR]

Application Programming Interfaces (APIs) have become common in contemporary software development. Many automated API recommendation methods have been proposed. However, these methods suffer from a deficit of using domain knowledge, giving rise to challenges like the "cold start" and "semantic gap" problems. Consequently, they are unsuitable for test function recommendation, which recommends test functions for test engineers to implement test cases formed with various test steps. This paper introduces an approach named TOKTER, which recommends test functions leveraging test-oriented knowledge graphs. Such a graph contains domain concepts and their relationships related to the system under test and the test harness, which is constructed from the corpus data of the concerned test project. TOKTER harnesses the semantic associations between test steps (or queries) and test functions by considering literal descriptions, test function parameters, and historical data. We evaluated TOKTER with an industrial dataset and compared it with three state-of-the-art approaches. Results show that TOKTER significantly outperformed the baseline by margins of at least 36.6% in mean average precision (MAP), 19.6% in mean reciprocal rank (MRR), and 1.9% in mean recall (MR) for the top-10 recommendations. [ABSTRACT FROM AUTHOR]

Featured Application: We present the ViPRAS system (Virtual Planning for Robotic-Assisted Surgery). It is the result of the project of the same name, in which we combine the advantages of collaborative virtual spaces with robot-assisted surgery. The system is designed to facilitate the surgical planning process through a virtual recreation of both the patient and the surgical robot. We believe this approach could improve surgical planning, allowing for better surgeries and thus better patient recovery. Robotic Assisted Surgery (RAS) represents an important step forward in the field of minimally invasive surgery. However, the learning curve of RAS is steep, and a systematic planning of surgical robot setups should be performed to leverage the features of RAS. For this reason, in this paper we show the design and implementation of a mixed-platform collaborative system, creating an interactive virtual shared environment that simulates RAS during the surgery planning phase. The proposed system allows one or more experts to plan together the different phases of an RAS-based surgical procedure, while integrating different levels of immersion to enhance computer-assisted training. We have tested our system with a total of four domain experts. Our results show that experts found the system excellent in terms of usability and useful to prepare and discuss surgical planning with RAS. [ABSTRACT FROM AUTHOR]

The ability of fast restoration reflects power system resilience and safety. This paper proposes a partitioning decision-making method for parallel restoration based on the label propagation algorithm and the cooperative game theory. The topological and physical characteristics of blackout systems are considered as the edge weight and the node importance simultaneously for formulating the partitioning model. By the label propagation algorithm, various labels representing different subsystems mark all buses. To accelerate the speed of convergence and prevent the label oscillation, this paper proposes a game strategy of cooperation between buses and subsystems by evaluating the Shapley value of buses. The partitioning constraints are integrated into each label propagation process to assess the feasibility of the partitioning strategy. Finally, case studies on the IEEE 39- and 118-bus test systems and an actual urban power system in South China verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents research findings on 3H in abiotic environmental compartments, specifically, the 'water-soil-air' system. All of the research areas are located within the Semipalatinsk Test Site (STS): the perimeter of the 'Degelen' site, the riverside zone of the Shagan river and the 'background' area–the southeastern part of the STS (SEP). As research progressed, numerical values of 3H and its species were revealed in various environmental compartments. The presence of 3H was registered not only in underground nuclear test locations but also in the 'background' area–SEP. Maximum 3H tritium concentrations in the water were detected at the 'Degelen' site (up to 57000±5000 Bq/kg) and the Shagan riv (up to 61500±6000 Bq/kg), in the air of the 'Degelen' site (up to 56±11 Bq/m3), in the soil of the 'Degelen' site (up to 5170±500 Bq/kg) and the Shagan riv (4100±400 Bq/kg) in the free water, at SEP (up to 1710±170 Bq/kg) in the organic constituent. Based upon all of the findings, 3H was found to be readily distributed in abiotic environmental compartments depending on certain conditions. Research suggests that water plays a key role in 3H migration processes in the natural system of interest. The second most but equally important constituent is soil and microorganisms of plant and animal origin living there. These assumptions are indirectly proven by research findings that show the HTO and HT air concentration dynamics depending on the sampling location. [ABSTRACT FROM AUTHOR]

Purpose: Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem. Design/methodology/approach: The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries. Findings: It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries. Research limitations/implications: The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data. Practical implications: The study concludes that Apache Hadoops' Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time. Originality/value: According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling. [ABSTRACT FROM AUTHOR]

Temperature is one of the most important physical quantities in the field of earthquake precursor observation. Aiming at the problem of low sensitivity in the Fiber Bragg Grating (FBG) temperature sensor, the sensitized FBG temperature sensor based on bimetal three-substrates is proposed. Through theoretical analysis of the bimetallic model, the structural parameters of the sensor are optimized, and the sensor is simulated and analyzed with ANSYS. Then, the sensor is developed according to the simulation results, and the temperature test system is built to test the performance of the sensor. The results show that the sensitivity of the temperature sensor is 49.3 pm/°C, which is about 4.9 times that of the bare FBG sensor, and the linearity is over 0.999. The research results provide a reference for developing the same type of sensors and further improving the sensitivity of FBG temperature sensors. [ABSTRACT FROM AUTHOR]

Our society is being transformed by the technology emergence and the industrial revolution. The advances in the internet and artificial intelligence are reshaping the means of education, profoundly changing the ways of teaching and learning. This paper studies the pattern of how the new 5th generation blended campus network is applied to aid the new generation of intelligence teaching. This pattern is the implementation of national major policies and the measure of cultivating people. This paper introduces a new model for the intelligence teaching system. Based on the new model, distance interaction teaching system, VR practicing teaching system, intelligence testing system, and higher education intelligence decision system are developed. This model can be the basis of the informatization of future education. [ABSTRACT FROM AUTHOR]